Overview

Brought to you by YData

Dataset statistics

Number of variables22
Number of observations244947
Missing cells0
Missing cells (%)0.0%
Duplicate rows41
Duplicate rows (%)< 0.1%
Total size in memory180.5 MiB
Average record size in memory772.5 B

Variable types

Numeric8
Text12
Categorical2

Alerts

YEAR has constant value "2021" Constant
Dataset has 41 (< 0.1%) duplicate rowsDuplicates
MONTH is highly overall correlated with QUARTERHigh correlation
QUARTER is highly overall correlated with MONTHHigh correlation
FREIGHT is highly skewed (γ1 = 22.95952665) Skewed
MAIL is highly skewed (γ1 = 26.24627126) Skewed
PASSENGERS has 47870 (19.5%) zeros Zeros
FREIGHT has 148788 (60.7%) zeros Zeros
MAIL has 214977 (87.8%) zeros Zeros

Reproduction

Analysis started2025-02-08 07:54:19.535969
Analysis finished2025-02-08 07:54:34.516328
Duration14.98 seconds
Software versionydata-profiling vv4.12.2
Download configurationconfig.json

Variables

PASSENGERS
Real number (ℝ)

Zeros 

Distinct20430
Distinct (%)8.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2482.9532
Minimum0
Maximum80604
Zeros47870
Zeros (%)19.5%
Negative0
Negative (%)0.0%
Memory size1.9 MiB
2025-02-08T08:54:34.656924image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q13
median188
Q32725
95-th percentile11841
Maximum80604
Range80604
Interquartile range (IQR)2722

Descriptive statistics

Standard deviation5352.7128
Coefficient of variation (CV)2.1557848
Kurtosis26.93965
Mean2482.9532
Median Absolute Deviation (MAD)188
Skewness4.4084815
Sum6.0819193 × 108
Variance28651534
MonotonicityIncreasing
2025-02-08T08:54:34.791778image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 47870
 
19.5%
2 5254
 
2.1%
1 5159
 
2.1%
3 3437
 
1.4%
4 3262
 
1.3%
5 2501
 
1.0%
6 2275
 
0.9%
7 1797
 
0.7%
8 1716
 
0.7%
9 1466
 
0.6%
Other values (20420) 170210
69.5%
ValueCountFrequency (%)
0 47870
19.5%
1 5159
 
2.1%
2 5254
 
2.1%
3 3437
 
1.4%
4 3262
 
1.3%
5 2501
 
1.0%
6 2275
 
0.9%
7 1797
 
0.7%
8 1716
 
0.7%
9 1466
 
0.6%
ValueCountFrequency (%)
80604 1
< 0.1%
77656 1
< 0.1%
77550 1
< 0.1%
77370 1
< 0.1%
77227 1
< 0.1%
74310 1
< 0.1%
72508 1
< 0.1%
72477 1
< 0.1%
71996 1
< 0.1%
71872 1
< 0.1%

FREIGHT
Real number (ℝ)

Skewed  Zeros 

Distinct46559
Distinct (%)19.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean119959.35
Minimum0
Maximum56068249
Zeros148788
Zeros (%)60.7%
Negative0
Negative (%)0.0%
Memory size1.9 MiB
2025-02-08T08:54:34.913373image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q32108.5
95-th percentile537720.8
Maximum56068249
Range56068249
Interquartile range (IQR)2108.5

Descriptive statistics

Standard deviation754374.32
Coefficient of variation (CV)6.2885827
Kurtosis1128.5976
Mean119959.35
Median Absolute Deviation (MAD)0
Skewness22.959527
Sum2.9383684 × 1010
Variance5.6908062 × 1011
MonotonicityNot monotonic
2025-02-08T08:54:35.025588image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 148788
60.7%
1 653
 
0.3%
2 444
 
0.2%
3 399
 
0.2%
5 311
 
0.1%
6 223
 
0.1%
4 222
 
0.1%
50 194
 
0.1%
10 188
 
0.1%
100 188
 
0.1%
Other values (46549) 93337
38.1%
ValueCountFrequency (%)
0 148788
60.7%
1 653
 
0.3%
2 444
 
0.2%
3 399
 
0.2%
4 222
 
0.1%
5 311
 
0.1%
6 223
 
0.1%
7 166
 
0.1%
8 148
 
0.1%
9 137
 
0.1%
ValueCountFrequency (%)
56068249 1
< 0.1%
55112371 1
< 0.1%
53886122 1
< 0.1%
53080707 1
< 0.1%
51587379 1
< 0.1%
50982983 1
< 0.1%
50432730 1
< 0.1%
50167937 1
< 0.1%
49904536 1
< 0.1%
48044236 1
< 0.1%

MAIL
Real number (ℝ)

Skewed  Zeros 

Distinct18190
Distinct (%)7.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean5499.439
Minimum0
Maximum5476382
Zeros214977
Zeros (%)87.8%
Negative0
Negative (%)0.0%
Memory size1.9 MiB
2025-02-08T08:54:35.134719image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile11930.5
Maximum5476382
Range5476382
Interquartile range (IQR)0

Descriptive statistics

Standard deviation52049.665
Coefficient of variation (CV)9.4645409
Kurtosis1198.5057
Mean5499.439
Median Absolute Deviation (MAD)0
Skewness26.246271
Sum1.3470711 × 109
Variance2.7091676 × 109
MonotonicityNot monotonic
2025-02-08T08:54:35.248730image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 214977
87.8%
1 76
 
< 0.1%
2 68
 
< 0.1%
3 60
 
< 0.1%
4 53
 
< 0.1%
8 52
 
< 0.1%
5 52
 
< 0.1%
6 49
 
< 0.1%
10 48
 
< 0.1%
18 42
 
< 0.1%
Other values (18180) 29470
 
12.0%
ValueCountFrequency (%)
0 214977
87.8%
1 76
 
< 0.1%
2 68
 
< 0.1%
3 60
 
< 0.1%
4 53
 
< 0.1%
5 52
 
< 0.1%
6 49
 
< 0.1%
7 33
 
< 0.1%
8 52
 
< 0.1%
9 34
 
< 0.1%
ValueCountFrequency (%)
5476382 1
< 0.1%
2838883 1
< 0.1%
2663075 1
< 0.1%
2648413 1
< 0.1%
2552068 1
< 0.1%
2550722 1
< 0.1%
2522643 1
< 0.1%
2508432 1
< 0.1%
2491430 1
< 0.1%
2474812 1
< 0.1%

DISTANCE
Real number (ℝ)

Distinct2762
Distinct (%)1.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean824.84629
Minimum0
Maximum7607
Zeros1478
Zeros (%)0.6%
Negative0
Negative (%)0.0%
Memory size1.9 MiB
2025-02-08T08:54:35.550639image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile67
Q1311
median667
Q31119
95-th percentile2286
Maximum7607
Range7607
Interquartile range (IQR)808

Descriptive statistics

Standard deviation677.3339
Coefficient of variation (CV)0.82116379
Kurtosis2.9684409
Mean824.84629
Median Absolute Deviation (MAD)390
Skewness1.4111487
Sum2.0204362 × 108
Variance458781.22
MonotonicityNot monotonic
2025-02-08T08:54:35.670530image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 1478
 
0.6%
95 672
 
0.3%
588 657
 
0.3%
337 612
 
0.2%
296 585
 
0.2%
335 559
 
0.2%
594 531
 
0.2%
861 485
 
0.2%
84 481
 
0.2%
192 476
 
0.2%
Other values (2752) 238411
97.3%
ValueCountFrequency (%)
0 1478
0.6%
2 4
 
< 0.1%
3 38
 
< 0.1%
4 16
 
< 0.1%
5 23
 
< 0.1%
6 98
 
< 0.1%
7 172
 
0.1%
8 161
 
0.1%
9 107
 
< 0.1%
10 96
 
< 0.1%
ValueCountFrequency (%)
7607 1
 
< 0.1%
7360 2
 
< 0.1%
7349 1
 
< 0.1%
7264 1
 
< 0.1%
7247 1
 
< 0.1%
7236 1
 
< 0.1%
6089 13
< 0.1%
6078 1
 
< 0.1%
5826 1
 
< 0.1%
5815 5
 
< 0.1%
Distinct112
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size11.9 MiB
2025-02-08T08:54:35.910360image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Length

Max length3
Median length2
Mean length2.1222305
Min length2

Characters and Unicode

Total characters519834
Distinct characters36
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row5X
2nd row5X
3rd row5X
4th row5X
5th row5X
ValueCountFrequency (%)
wn 42763
 
17.5%
oo 14106
 
5.8%
dl 11670
 
4.8%
g4 11129
 
4.5%
fx 10983
 
4.5%
ua 9455
 
3.9%
09q 8811
 
3.6%
aa 8777
 
3.6%
5x 8138
 
3.3%
yx 8109
 
3.3%
Other values (102) 111006
45.3%
2025-02-08T08:54:36.188018image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
N 50299
 
9.7%
W 45586
 
8.8%
A 38360
 
7.4%
Q 35254
 
6.8%
O 34596
 
6.7%
X 32312
 
6.2%
F 28482
 
5.5%
9 22194
 
4.3%
Y 18938
 
3.6%
L 16467
 
3.2%
Other values (26) 197346
38.0%

Most occurring categories

ValueCountFrequency (%)
(unknown) 519834
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
N 50299
 
9.7%
W 45586
 
8.8%
A 38360
 
7.4%
Q 35254
 
6.8%
O 34596
 
6.7%
X 32312
 
6.2%
F 28482
 
5.5%
9 22194
 
4.3%
Y 18938
 
3.6%
L 16467
 
3.2%
Other values (26) 197346
38.0%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 519834
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
N 50299
 
9.7%
W 45586
 
8.8%
A 38360
 
7.4%
Q 35254
 
6.8%
O 34596
 
6.7%
X 32312
 
6.2%
F 28482
 
5.5%
9 22194
 
4.3%
Y 18938
 
3.6%
L 16467
 
3.2%
Other values (26) 197346
38.0%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 519834
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
N 50299
 
9.7%
W 45586
 
8.8%
A 38360
 
7.4%
Q 35254
 
6.8%
O 34596
 
6.7%
X 32312
 
6.2%
F 28482
 
5.5%
9 22194
 
4.3%
Y 18938
 
3.6%
L 16467
 
3.2%
Other values (26) 197346
38.0%
Distinct112
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size17.0 MiB
2025-02-08T08:54:36.455905image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Length

Max length80
Median length67
Mean length23.802786
Min length8

Characters and Unicode

Total characters5830421
Distinct characters59
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowUnited Parcel Service
2nd rowUnited Parcel Service
3rd rowUnited Parcel Service
4th rowUnited Parcel Service
5th rowUnited Parcel Service
ValueCountFrequency (%)
airlines 107443
 
12.0%
air 107012
 
12.0%
inc 96383
 
10.8%
d/b/a 46180
 
5.2%
southwest 42763
 
4.8%
co 42763
 
4.8%
lines 35122
 
3.9%
united 19323
 
2.2%
llc 17685
 
2.0%
eastern 17658
 
2.0%
Other values (183) 363158
40.6%
2025-02-08T08:54:36.854800image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
670631
 
11.5%
i 552891
 
9.5%
e 485017
 
8.3%
r 442791
 
7.6%
n 418292
 
7.2%
t 306735
 
5.3%
s 302915
 
5.2%
A 290202
 
5.0%
a 249338
 
4.3%
l 222187
 
3.8%
Other values (49) 1889422
32.4%

Most occurring categories

ValueCountFrequency (%)
(unknown) 5830421
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
670631
 
11.5%
i 552891
 
9.5%
e 485017
 
8.3%
r 442791
 
7.6%
n 418292
 
7.2%
t 306735
 
5.3%
s 302915
 
5.2%
A 290202
 
5.0%
a 249338
 
4.3%
l 222187
 
3.8%
Other values (49) 1889422
32.4%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 5830421
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
670631
 
11.5%
i 552891
 
9.5%
e 485017
 
8.3%
r 442791
 
7.6%
n 418292
 
7.2%
t 306735
 
5.3%
s 302915
 
5.2%
A 290202
 
5.0%
a 249338
 
4.3%
l 222187
 
3.8%
Other values (49) 1889422
32.4%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 5830421
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
670631
 
11.5%
i 552891
 
9.5%
e 485017
 
8.3%
r 442791
 
7.6%
n 418292
 
7.2%
t 306735
 
5.3%
s 302915
 
5.2%
A 290202
 
5.0%
a 249338
 
4.3%
l 222187
 
3.8%
Other values (49) 1889422
32.4%
Distinct112
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size11.9 MiB
2025-02-08T08:54:37.037336image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Length

Max length3
Median length2
Mean length2.1222305
Min length2

Characters and Unicode

Total characters519834
Distinct characters36
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row5X
2nd row5X
3rd row5X
4th row5X
5th row5X
ValueCountFrequency (%)
wn 42763
 
17.5%
oo 14106
 
5.8%
dl 11670
 
4.8%
g4 11129
 
4.5%
fx 10983
 
4.5%
ua 9455
 
3.9%
09q 8811
 
3.6%
aa 8777
 
3.6%
5x 8138
 
3.3%
yx 8109
 
3.3%
Other values (102) 111006
45.3%
2025-02-08T08:54:37.316065image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
N 50299
 
9.7%
W 45586
 
8.8%
A 38360
 
7.4%
Q 35254
 
6.8%
O 34596
 
6.7%
X 32312
 
6.2%
F 28482
 
5.5%
9 22194
 
4.3%
Y 18938
 
3.6%
L 16467
 
3.2%
Other values (26) 197346
38.0%

Most occurring categories

ValueCountFrequency (%)
(unknown) 519834
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
N 50299
 
9.7%
W 45586
 
8.8%
A 38360
 
7.4%
Q 35254
 
6.8%
O 34596
 
6.7%
X 32312
 
6.2%
F 28482
 
5.5%
9 22194
 
4.3%
Y 18938
 
3.6%
L 16467
 
3.2%
Other values (26) 197346
38.0%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 519834
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
N 50299
 
9.7%
W 45586
 
8.8%
A 38360
 
7.4%
Q 35254
 
6.8%
O 34596
 
6.7%
X 32312
 
6.2%
F 28482
 
5.5%
9 22194
 
4.3%
Y 18938
 
3.6%
L 16467
 
3.2%
Other values (26) 197346
38.0%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 519834
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
N 50299
 
9.7%
W 45586
 
8.8%
A 38360
 
7.4%
Q 35254
 
6.8%
O 34596
 
6.7%
X 32312
 
6.2%
F 28482
 
5.5%
9 22194
 
4.3%
Y 18938
 
3.6%
L 16467
 
3.2%
Other values (26) 197346
38.0%
Distinct114
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size17.0 MiB
2025-02-08T08:54:37.533407image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Length

Max length80
Median length67
Mean length23.81665
Min length8

Characters and Unicode

Total characters5833817
Distinct characters59
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowUnited Parcel Service
2nd rowUnited Parcel Service
3rd rowUnited Parcel Service
4th rowUnited Parcel Service
5th rowUnited Parcel Service
ValueCountFrequency (%)
airlines 107819
 
12.0%
air 107012
 
11.9%
inc 96383
 
10.8%
d/b/a 46988
 
5.2%
southwest 42763
 
4.8%
co 42763
 
4.8%
lines 35122
 
3.9%
united 19323
 
2.2%
llc 17685
 
2.0%
eastern 17658
 
2.0%
Other values (186) 362698
40.5%
2025-02-08T08:54:37.872040image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
670923
 
11.5%
i 553239
 
9.5%
e 484905
 
8.3%
r 442735
 
7.6%
n 418640
 
7.2%
t 306707
 
5.3%
s 303263
 
5.2%
A 290606
 
5.0%
a 249742
 
4.3%
l 222535
 
3.8%
Other values (49) 1890522
32.4%

Most occurring categories

ValueCountFrequency (%)
(unknown) 5833817
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
670923
 
11.5%
i 553239
 
9.5%
e 484905
 
8.3%
r 442735
 
7.6%
n 418640
 
7.2%
t 306707
 
5.3%
s 303263
 
5.2%
A 290606
 
5.0%
a 249742
 
4.3%
l 222535
 
3.8%
Other values (49) 1890522
32.4%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 5833817
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
670923
 
11.5%
i 553239
 
9.5%
e 484905
 
8.3%
r 442735
 
7.6%
n 418640
 
7.2%
t 306707
 
5.3%
s 303263
 
5.2%
A 290606
 
5.0%
a 249742
 
4.3%
l 222535
 
3.8%
Other values (49) 1890522
32.4%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 5833817
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
670923
 
11.5%
i 553239
 
9.5%
e 484905
 
8.3%
r 442735
 
7.6%
n 418640
 
7.2%
t 306707
 
5.3%
s 303263
 
5.2%
A 290606
 
5.0%
a 249742
 
4.3%
l 222535
 
3.8%
Other values (49) 1890522
32.4%

CARRIER_GROUP_NEW
Real number (ℝ)

Distinct7
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean3.0164444
Minimum1
Maximum9
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.9 MiB
2025-02-08T08:54:37.951771image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q13
median3
Q33
95-th percentile6
Maximum9
Range8
Interquartile range (IQR)0

Descriptive statistics

Standard deviation1.0863222
Coefficient of variation (CV)0.36013334
Kurtosis1.8928286
Mean3.0164444
Median Absolute Deviation (MAD)0
Skewness1.0411301
Sum738869
Variance1.1800959
MonotonicityNot monotonic
2025-02-08T08:54:38.031251image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=7)
ValueCountFrequency (%)
3 156594
63.9%
2 41745
 
17.0%
5 17962
 
7.3%
1 14715
 
6.0%
6 12449
 
5.1%
4 1392
 
0.6%
9 90
 
< 0.1%
ValueCountFrequency (%)
1 14715
 
6.0%
2 41745
 
17.0%
3 156594
63.9%
4 1392
 
0.6%
5 17962
 
7.3%
6 12449
 
5.1%
9 90
 
< 0.1%
ValueCountFrequency (%)
9 90
 
< 0.1%
6 12449
 
5.1%
5 17962
 
7.3%
4 1392
 
0.6%
3 156594
63.9%
2 41745
 
17.0%
1 14715
 
6.0%

ORIGIN_AIRPORT_ID
Real number (ℝ)

Distinct1361
Distinct (%)0.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean12782.168
Minimum10001
Maximum16942
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.9 MiB
2025-02-08T08:54:38.129804image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum10001
5-th percentile10397
Q111292
median12892
Q314107
95-th percentile15167
Maximum16942
Range6941
Interquartile range (IQR)2815

Descriptive statistics

Standard deviation1592.1095
Coefficient of variation (CV)0.12455708
Kurtosis-1.2297215
Mean12782.168
Median Absolute Deviation (MAD)1459
Skewness0.024466697
Sum3.1309536 × 109
Variance2534812.6
MonotonicityNot monotonic
2025-02-08T08:54:38.233848image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
13930 6986
 
2.9%
11292 5579
 
2.3%
11298 4423
 
1.8%
10397 4362
 
1.8%
12892 4242
 
1.7%
12889 3904
 
1.6%
12266 3738
 
1.5%
14107 3718
 
1.5%
11057 3676
 
1.5%
13487 3481
 
1.4%
Other values (1351) 200838
82.0%
ValueCountFrequency (%)
10001 3
 
< 0.1%
10005 1
 
< 0.1%
10006 14
< 0.1%
10009 9
< 0.1%
10010 4
 
< 0.1%
10011 22
< 0.1%
10014 7
 
< 0.1%
10015 1
 
< 0.1%
10016 8
 
< 0.1%
10017 4
 
< 0.1%
ValueCountFrequency (%)
16942 1
 
< 0.1%
16941 2
 
< 0.1%
16940 1
 
< 0.1%
16938 1
 
< 0.1%
16936 2
 
< 0.1%
16935 2
 
< 0.1%
16934 11
< 0.1%
16929 5
< 0.1%
16927 2
 
< 0.1%
16926 1
 
< 0.1%

ORIGIN
Text

Distinct1361
Distinct (%)0.6%
Missing0
Missing (%)0.0%
Memory size12.1 MiB
2025-02-08T08:54:38.516308image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters734841
Distinct characters36
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique215 ?
Unique (%)0.1%

Sample

1st rowONT
2nd rowONT
3rd rowONT
4th rowONT
5th rowONT
ValueCountFrequency (%)
ord 6986
 
2.9%
den 5579
 
2.3%
dfw 4423
 
1.8%
atl 4362
 
1.8%
lax 4242
 
1.7%
las 3904
 
1.6%
iah 3738
 
1.5%
phx 3718
 
1.5%
clt 3676
 
1.5%
msp 3481
 
1.4%
Other values (1351) 200838
82.0%
2025-02-08T08:54:38.875093image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
A 71407
 
9.7%
S 58587
 
8.0%
L 51548
 
7.0%
D 47465
 
6.5%
M 38339
 
5.2%
C 37824
 
5.1%
T 37614
 
5.1%
P 34086
 
4.6%
O 34040
 
4.6%
I 31989
 
4.4%
Other values (26) 291942
39.7%

Most occurring categories

ValueCountFrequency (%)
(unknown) 734841
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
A 71407
 
9.7%
S 58587
 
8.0%
L 51548
 
7.0%
D 47465
 
6.5%
M 38339
 
5.2%
C 37824
 
5.1%
T 37614
 
5.1%
P 34086
 
4.6%
O 34040
 
4.6%
I 31989
 
4.4%
Other values (26) 291942
39.7%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 734841
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
A 71407
 
9.7%
S 58587
 
8.0%
L 51548
 
7.0%
D 47465
 
6.5%
M 38339
 
5.2%
C 37824
 
5.1%
T 37614
 
5.1%
P 34086
 
4.6%
O 34040
 
4.6%
I 31989
 
4.4%
Other values (26) 291942
39.7%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 734841
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
A 71407
 
9.7%
S 58587
 
8.0%
L 51548
 
7.0%
D 47465
 
6.5%
M 38339
 
5.2%
C 37824
 
5.1%
T 37614
 
5.1%
P 34086
 
4.6%
O 34040
 
4.6%
I 31989
 
4.4%
Other values (26) 291942
39.7%
Distinct1229
Distinct (%)0.5%
Missing0
Missing (%)0.0%
Memory size14.5 MiB
2025-02-08T08:54:39.088236image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Length

Max length34
Median length28
Mean length13.204987
Min length7

Characters and Unicode

Total characters3234522
Distinct characters59
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique186 ?
Unique (%)0.1%

Sample

1st rowOntario, CA
2nd rowOntario, CA
3rd rowOntario, CA
4th rowOntario, CA
5th rowOntario, CA
ValueCountFrequency (%)
ca 22789
 
4.0%
fl 22643
 
4.0%
ak 21645
 
3.8%
tx 20465
 
3.6%
il 10120
 
1.8%
ny 9197
 
1.6%
san 9104
 
1.6%
chicago 8539
 
1.5%
city 8114
 
1.4%
co 7680
 
1.4%
Other values (1265) 422980
75.1%
2025-02-08T08:54:39.408318image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
318329
 
9.8%
, 244947
 
7.6%
a 240340
 
7.4%
e 173057
 
5.4%
o 170134
 
5.3%
n 164048
 
5.1%
i 142655
 
4.4%
l 137041
 
4.2%
t 133507
 
4.1%
r 114687
 
3.5%
Other values (49) 1395777
43.2%

Most occurring categories

ValueCountFrequency (%)
(unknown) 3234522
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
318329
 
9.8%
, 244947
 
7.6%
a 240340
 
7.4%
e 173057
 
5.4%
o 170134
 
5.3%
n 164048
 
5.1%
i 142655
 
4.4%
l 137041
 
4.2%
t 133507
 
4.1%
r 114687
 
3.5%
Other values (49) 1395777
43.2%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 3234522
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
318329
 
9.8%
, 244947
 
7.6%
a 240340
 
7.4%
e 173057
 
5.4%
o 170134
 
5.3%
n 164048
 
5.1%
i 142655
 
4.4%
l 137041
 
4.2%
t 133507
 
4.1%
r 114687
 
3.5%
Other values (49) 1395777
43.2%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 3234522
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
318329
 
9.8%
, 244947
 
7.6%
a 240340
 
7.4%
e 173057
 
5.4%
o 170134
 
5.3%
n 164048
 
5.1%
i 142655
 
4.4%
l 137041
 
4.2%
t 133507
 
4.1%
r 114687
 
3.5%
Other values (49) 1395777
43.2%
Distinct53
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size11.9 MiB
2025-02-08T08:54:39.570061image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Length

Max length2
Median length2
Mean length2
Min length2

Characters and Unicode

Total characters489894
Distinct characters24
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowCA
2nd rowCA
3rd rowCA
4th rowCA
5th rowCA
ValueCountFrequency (%)
ca 22789
 
9.3%
fl 22643
 
9.2%
ak 21645
 
8.8%
tx 20465
 
8.4%
il 10120
 
4.1%
ny 9197
 
3.8%
co 7680
 
3.1%
va 7501
 
3.1%
nc 7294
 
3.0%
pa 6534
 
2.7%
Other values (43) 109079
44.5%
2025-02-08T08:54:39.815626image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
A 87460
17.9%
N 44362
 
9.1%
C 42453
 
8.7%
L 36847
 
7.5%
T 34494
 
7.0%
K 29678
 
6.1%
I 28332
 
5.8%
M 25066
 
5.1%
F 22643
 
4.6%
O 21292
 
4.3%
Other values (14) 117267
23.9%

Most occurring categories

ValueCountFrequency (%)
(unknown) 489894
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
A 87460
17.9%
N 44362
 
9.1%
C 42453
 
8.7%
L 36847
 
7.5%
T 34494
 
7.0%
K 29678
 
6.1%
I 28332
 
5.8%
M 25066
 
5.1%
F 22643
 
4.6%
O 21292
 
4.3%
Other values (14) 117267
23.9%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 489894
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
A 87460
17.9%
N 44362
 
9.1%
C 42453
 
8.7%
L 36847
 
7.5%
T 34494
 
7.0%
K 29678
 
6.1%
I 28332
 
5.8%
M 25066
 
5.1%
F 22643
 
4.6%
O 21292
 
4.3%
Other values (14) 117267
23.9%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 489894
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
A 87460
17.9%
N 44362
 
9.1%
C 42453
 
8.7%
L 36847
 
7.5%
T 34494
 
7.0%
K 29678
 
6.1%
I 28332
 
5.8%
M 25066
 
5.1%
F 22643
 
4.6%
O 21292
 
4.3%
Other values (14) 117267
23.9%
Distinct53
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size13.3 MiB
2025-02-08T08:54:39.982159image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Length

Max length46
Median length14
Mean length8.1184419
Min length4

Characters and Unicode

Total characters1988588
Distinct characters47
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowCalifornia
2nd rowCalifornia
3rd rowCalifornia
4th rowCalifornia
5th rowCalifornia
ValueCountFrequency (%)
california 22789
 
8.2%
florida 22643
 
8.1%
alaska 21645
 
7.8%
texas 20465
 
7.3%
new 15948
 
5.7%
carolina 10658
 
3.8%
illinois 10120
 
3.6%
york 9197
 
3.3%
north 8183
 
2.9%
virginia 7916
 
2.8%
Other values (52) 129638
46.4%
2025-02-08T08:54:40.248129image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
a 283034
14.2%
i 208969
 
10.5%
o 167353
 
8.4%
n 153898
 
7.7%
s 134335
 
6.8%
r 123285
 
6.2%
l 119289
 
6.0%
e 116412
 
5.9%
t 48153
 
2.4%
d 45145
 
2.3%
Other values (37) 588715
29.6%

Most occurring categories

ValueCountFrequency (%)
(unknown) 1988588
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
a 283034
14.2%
i 208969
 
10.5%
o 167353
 
8.4%
n 153898
 
7.7%
s 134335
 
6.8%
r 123285
 
6.2%
l 119289
 
6.0%
e 116412
 
5.9%
t 48153
 
2.4%
d 45145
 
2.3%
Other values (37) 588715
29.6%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 1988588
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
a 283034
14.2%
i 208969
 
10.5%
o 167353
 
8.4%
n 153898
 
7.7%
s 134335
 
6.8%
r 123285
 
6.2%
l 119289
 
6.0%
e 116412
 
5.9%
t 48153
 
2.4%
d 45145
 
2.3%
Other values (37) 588715
29.6%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 1988588
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
a 283034
14.2%
i 208969
 
10.5%
o 167353
 
8.4%
n 153898
 
7.7%
s 134335
 
6.8%
r 123285
 
6.2%
l 119289
 
6.0%
e 116412
 
5.9%
t 48153
 
2.4%
d 45145
 
2.3%
Other values (37) 588715
29.6%

DEST_AIRPORT_ID
Real number (ℝ)

Distinct1374
Distinct (%)0.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean12790.298
Minimum10001
Maximum16942
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.9 MiB
2025-02-08T08:54:40.361522image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum10001
5-th percentile10397
Q111292
median12892
Q314107
95-th percentile15167
Maximum16942
Range6941
Interquartile range (IQR)2815

Descriptive statistics

Standard deviation1594.2714
Coefficient of variation (CV)0.12464693
Kurtosis-1.2252065
Mean12790.298
Median Absolute Deviation (MAD)1459
Skewness0.022586755
Sum3.1329452 × 109
Variance2541701.4
MonotonicityNot monotonic
2025-02-08T08:54:40.480271image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
13930 7030
 
2.9%
11292 5569
 
2.3%
11298 4529
 
1.8%
10397 4407
 
1.8%
12892 4240
 
1.7%
12889 3857
 
1.6%
12266 3788
 
1.5%
14107 3781
 
1.5%
11057 3648
 
1.5%
14100 3455
 
1.4%
Other values (1364) 200643
81.9%
ValueCountFrequency (%)
10001 3
 
< 0.1%
10005 1
 
< 0.1%
10006 12
 
< 0.1%
10009 9
 
< 0.1%
10010 3
 
< 0.1%
10011 38
< 0.1%
10014 8
 
< 0.1%
10016 7
 
< 0.1%
10017 4
 
< 0.1%
10030 1
 
< 0.1%
ValueCountFrequency (%)
16942 1
 
< 0.1%
16941 2
 
< 0.1%
16940 1
 
< 0.1%
16938 1
 
< 0.1%
16937 1
 
< 0.1%
16936 2
 
< 0.1%
16935 2
 
< 0.1%
16934 13
< 0.1%
16929 5
 
< 0.1%
16927 1
 
< 0.1%

DEST
Text

Distinct1374
Distinct (%)0.6%
Missing0
Missing (%)0.0%
Memory size12.1 MiB
2025-02-08T08:54:40.811970image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters734841
Distinct characters36
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique208 ?
Unique (%)0.1%

Sample

1st rowPVD
2nd rowPIT
3rd rowLAN
4th rowDFW
5th rowBDL
ValueCountFrequency (%)
ord 7030
 
2.9%
den 5569
 
2.3%
dfw 4529
 
1.8%
atl 4407
 
1.8%
lax 4240
 
1.7%
las 3857
 
1.6%
iah 3788
 
1.5%
phx 3781
 
1.5%
clt 3648
 
1.5%
phl 3455
 
1.4%
Other values (1364) 200643
81.9%
2025-02-08T08:54:41.221681image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
A 71016
 
9.7%
S 58466
 
8.0%
L 51680
 
7.0%
D 47502
 
6.5%
M 38272
 
5.2%
C 38045
 
5.2%
T 37317
 
5.1%
O 34178
 
4.7%
P 34018
 
4.6%
N 31510
 
4.3%
Other values (26) 292837
39.9%

Most occurring categories

ValueCountFrequency (%)
(unknown) 734841
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
A 71016
 
9.7%
S 58466
 
8.0%
L 51680
 
7.0%
D 47502
 
6.5%
M 38272
 
5.2%
C 38045
 
5.2%
T 37317
 
5.1%
O 34178
 
4.7%
P 34018
 
4.6%
N 31510
 
4.3%
Other values (26) 292837
39.9%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 734841
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
A 71016
 
9.7%
S 58466
 
8.0%
L 51680
 
7.0%
D 47502
 
6.5%
M 38272
 
5.2%
C 38045
 
5.2%
T 37317
 
5.1%
O 34178
 
4.7%
P 34018
 
4.6%
N 31510
 
4.3%
Other values (26) 292837
39.9%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 734841
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
A 71016
 
9.7%
S 58466
 
8.0%
L 51680
 
7.0%
D 47502
 
6.5%
M 38272
 
5.2%
C 38045
 
5.2%
T 37317
 
5.1%
O 34178
 
4.7%
P 34018
 
4.6%
N 31510
 
4.3%
Other values (26) 292837
39.9%
Distinct1245
Distinct (%)0.5%
Missing0
Missing (%)0.0%
Memory size14.5 MiB
2025-02-08T08:54:41.480898image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Length

Max length34
Median length28
Mean length13.208261
Min length7

Characters and Unicode

Total characters3235324
Distinct characters59
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique181 ?
Unique (%)0.1%

Sample

1st rowProvidence, RI
2nd rowPittsburgh, PA
3rd rowLansing, MI
4th rowDallas/Fort Worth, TX
5th rowHartford, CT
ValueCountFrequency (%)
ca 23129
 
4.1%
fl 22240
 
3.9%
ak 21571
 
3.8%
tx 20627
 
3.7%
il 10251
 
1.8%
ny 9161
 
1.6%
san 9108
 
1.6%
chicago 8595
 
1.5%
city 8153
 
1.4%
co 7787
 
1.4%
Other values (1278) 422797
75.0%
2025-02-08T08:54:41.849612image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
318472
 
9.8%
, 244947
 
7.6%
a 240046
 
7.4%
e 171970
 
5.3%
o 170340
 
5.3%
n 164527
 
5.1%
i 143643
 
4.4%
l 136989
 
4.2%
t 133489
 
4.1%
r 114974
 
3.6%
Other values (49) 1395927
43.1%

Most occurring categories

ValueCountFrequency (%)
(unknown) 3235324
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
318472
 
9.8%
, 244947
 
7.6%
a 240046
 
7.4%
e 171970
 
5.3%
o 170340
 
5.3%
n 164527
 
5.1%
i 143643
 
4.4%
l 136989
 
4.2%
t 133489
 
4.1%
r 114974
 
3.6%
Other values (49) 1395927
43.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 3235324
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
318472
 
9.8%
, 244947
 
7.6%
a 240046
 
7.4%
e 171970
 
5.3%
o 170340
 
5.3%
n 164527
 
5.1%
i 143643
 
4.4%
l 136989
 
4.2%
t 133489
 
4.1%
r 114974
 
3.6%
Other values (49) 1395927
43.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 3235324
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
318472
 
9.8%
, 244947
 
7.6%
a 240046
 
7.4%
e 171970
 
5.3%
o 170340
 
5.3%
n 164527
 
5.1%
i 143643
 
4.4%
l 136989
 
4.2%
t 133489
 
4.1%
r 114974
 
3.6%
Other values (49) 1395927
43.1%
Distinct53
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size11.9 MiB
2025-02-08T08:54:42.007905image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Length

Max length2
Median length2
Mean length2
Min length2

Characters and Unicode

Total characters489894
Distinct characters24
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowRI
2nd rowPA
3rd rowMI
4th rowTX
5th rowCT
ValueCountFrequency (%)
ca 23129
 
9.4%
fl 22240
 
9.1%
ak 21571
 
8.8%
tx 20627
 
8.4%
il 10251
 
4.2%
ny 9161
 
3.7%
co 7787
 
3.2%
va 7417
 
3.0%
nc 7209
 
2.9%
pa 6651
 
2.7%
Other values (43) 108904
44.5%
2025-02-08T08:54:42.253604image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
A 87690
17.9%
N 44318
 
9.0%
C 42790
 
8.7%
L 36692
 
7.5%
T 34753
 
7.1%
K 29800
 
6.1%
I 28284
 
5.8%
M 24594
 
5.0%
F 22240
 
4.5%
O 21520
 
4.4%
Other values (14) 117213
23.9%

Most occurring categories

ValueCountFrequency (%)
(unknown) 489894
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
A 87690
17.9%
N 44318
 
9.0%
C 42790
 
8.7%
L 36692
 
7.5%
T 34753
 
7.1%
K 29800
 
6.1%
I 28284
 
5.8%
M 24594
 
5.0%
F 22240
 
4.5%
O 21520
 
4.4%
Other values (14) 117213
23.9%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 489894
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
A 87690
17.9%
N 44318
 
9.0%
C 42790
 
8.7%
L 36692
 
7.5%
T 34753
 
7.1%
K 29800
 
6.1%
I 28284
 
5.8%
M 24594
 
5.0%
F 22240
 
4.5%
O 21520
 
4.4%
Other values (14) 117213
23.9%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 489894
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
A 87690
17.9%
N 44318
 
9.0%
C 42790
 
8.7%
L 36692
 
7.5%
T 34753
 
7.1%
K 29800
 
6.1%
I 28284
 
5.8%
M 24594
 
5.0%
F 22240
 
4.5%
O 21520
 
4.4%
Other values (14) 117213
23.9%
Distinct53
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size13.3 MiB
2025-02-08T08:54:42.422967image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Length

Max length46
Median length14
Mean length8.1179031
Min length4

Characters and Unicode

Total characters1988456
Distinct characters47
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowRhode Island
2nd rowPennsylvania
3rd rowMichigan
4th rowTexas
5th rowConnecticut
ValueCountFrequency (%)
california 23129
 
8.3%
florida 22240
 
8.0%
alaska 21571
 
7.7%
texas 20627
 
7.4%
new 15859
 
5.7%
carolina 10604
 
3.8%
illinois 10251
 
3.7%
york 9161
 
3.3%
north 8065
 
2.9%
virginia 7847
 
2.8%
Other values (52) 129649
46.5%
2025-02-08T08:54:42.695696image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
a 282795
14.2%
i 208759
 
10.5%
o 167152
 
8.4%
n 154302
 
7.8%
s 134524
 
6.8%
r 123195
 
6.2%
l 119573
 
6.0%
e 117224
 
5.9%
t 47615
 
2.4%
d 44933
 
2.3%
Other values (37) 588384
29.6%

Most occurring categories

ValueCountFrequency (%)
(unknown) 1988456
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
a 282795
14.2%
i 208759
 
10.5%
o 167152
 
8.4%
n 154302
 
7.8%
s 134524
 
6.8%
r 123195
 
6.2%
l 119573
 
6.0%
e 117224
 
5.9%
t 47615
 
2.4%
d 44933
 
2.3%
Other values (37) 588384
29.6%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 1988456
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
a 282795
14.2%
i 208759
 
10.5%
o 167152
 
8.4%
n 154302
 
7.8%
s 134524
 
6.8%
r 123195
 
6.2%
l 119573
 
6.0%
e 117224
 
5.9%
t 47615
 
2.4%
d 44933
 
2.3%
Other values (37) 588384
29.6%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 1988456
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
a 282795
14.2%
i 208759
 
10.5%
o 167152
 
8.4%
n 154302
 
7.8%
s 134524
 
6.8%
r 123195
 
6.2%
l 119573
 
6.0%
e 117224
 
5.9%
t 47615
 
2.4%
d 44933
 
2.3%
Other values (37) 588384
29.6%

YEAR
Categorical

Constant 

Distinct1
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size12.4 MiB
2021
244947 

Length

Max length4
Median length4
Mean length4
Min length4

Characters and Unicode

Total characters979788
Distinct characters3
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row2021
2nd row2021
3rd row2021
4th row2021
5th row2021

Common Values

ValueCountFrequency (%)
2021 244947
100.0%

Length

2025-02-08T08:54:42.802821image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-02-08T08:54:42.886963image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
ValueCountFrequency (%)
2021 244947
100.0%

Most occurring characters

ValueCountFrequency (%)
2 489894
50.0%
0 244947
25.0%
1 244947
25.0%

Most occurring categories

ValueCountFrequency (%)
(unknown) 979788
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
2 489894
50.0%
0 244947
25.0%
1 244947
25.0%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 979788
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
2 489894
50.0%
0 244947
25.0%
1 244947
25.0%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 979788
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
2 489894
50.0%
0 244947
25.0%
1 244947
25.0%

QUARTER
Categorical

High correlation 

Distinct4
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size11.7 MiB
4
68046 
3
65648 
2
59855 
1
51398 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters244947
Distinct characters4
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row4
2nd row4
3rd row4
4th row4
5th row4

Common Values

ValueCountFrequency (%)
4 68046
27.8%
3 65648
26.8%
2 59855
24.4%
1 51398
21.0%

Length

2025-02-08T08:54:42.966973image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-02-08T08:54:43.048405image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
ValueCountFrequency (%)
4 68046
27.8%
3 65648
26.8%
2 59855
24.4%
1 51398
21.0%

Most occurring characters

ValueCountFrequency (%)
4 68046
27.8%
3 65648
26.8%
2 59855
24.4%
1 51398
21.0%

Most occurring categories

ValueCountFrequency (%)
(unknown) 244947
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
4 68046
27.8%
3 65648
26.8%
2 59855
24.4%
1 51398
21.0%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 244947
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
4 68046
27.8%
3 65648
26.8%
2 59855
24.4%
1 51398
21.0%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 244947
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
4 68046
27.8%
3 65648
26.8%
2 59855
24.4%
1 51398
21.0%

MONTH
Real number (ℝ)

High correlation 

Distinct12
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean6.8613292
Minimum1
Maximum12
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.9 MiB
2025-02-08T08:54:43.133693image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q14
median7
Q310
95-th percentile12
Maximum12
Range11
Interquartile range (IQR)6

Descriptive statistics

Standard deviation3.3905954
Coefficient of variation (CV)0.49416014
Kurtosis-1.1478747
Mean6.8613292
Median Absolute Deviation (MAD)3
Skewness-0.13468341
Sum1680662
Variance11.496137
MonotonicityNot monotonic
2025-02-08T08:54:43.221437image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=12)
ValueCountFrequency (%)
11 22834
9.3%
12 22636
9.2%
10 22576
9.2%
9 22089
9.0%
6 22030
9.0%
7 21800
8.9%
8 21759
8.9%
5 19291
7.9%
4 18534
7.6%
3 18383
7.5%
Other values (2) 33015
13.5%
ValueCountFrequency (%)
1 17327
7.1%
2 15688
6.4%
3 18383
7.5%
4 18534
7.6%
5 19291
7.9%
6 22030
9.0%
7 21800
8.9%
8 21759
8.9%
9 22089
9.0%
10 22576
9.2%
ValueCountFrequency (%)
12 22636
9.2%
11 22834
9.3%
10 22576
9.2%
9 22089
9.0%
8 21759
8.9%
7 21800
8.9%
6 22030
9.0%
5 19291
7.9%
4 18534
7.6%
3 18383
7.5%

Interactions

2025-02-08T08:54:32.379922image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-02-08T08:54:25.686797image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-02-08T08:54:26.636989image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-02-08T08:54:27.676579image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-02-08T08:54:28.565889image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-02-08T08:54:29.466097image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-02-08T08:54:30.270039image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-02-08T08:54:31.399849image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-02-08T08:54:32.494781image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-02-08T08:54:25.811078image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-02-08T08:54:26.749996image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-02-08T08:54:27.790429image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-02-08T08:54:28.688688image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-02-08T08:54:29.571114image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-02-08T08:54:30.580756image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-02-08T08:54:31.524616image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-02-08T08:54:32.589127image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-02-08T08:54:25.933582image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-02-08T08:54:26.989148image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-02-08T08:54:27.896817image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-02-08T08:54:28.797310image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-02-08T08:54:29.668053image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-02-08T08:54:30.692245image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-02-08T08:54:31.636738image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-02-08T08:54:32.690925image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-02-08T08:54:26.049235image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-02-08T08:54:27.094544image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-02-08T08:54:28.004617image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-02-08T08:54:28.907023image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-02-08T08:54:29.762080image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-02-08T08:54:30.797200image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-02-08T08:54:31.765940image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-02-08T08:54:32.805527image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-02-08T08:54:26.175336image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-02-08T08:54:27.216922image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-02-08T08:54:28.122258image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-02-08T08:54:29.022755image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-02-08T08:54:29.858641image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-02-08T08:54:30.918168image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-02-08T08:54:31.895292image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-02-08T08:54:32.917113image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-02-08T08:54:26.284142image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-02-08T08:54:27.335324image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-02-08T08:54:28.233994image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-02-08T08:54:29.147215image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-02-08T08:54:29.963070image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-02-08T08:54:31.042485image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-02-08T08:54:32.016154image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-02-08T08:54:33.037730image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-02-08T08:54:26.405408image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-02-08T08:54:27.447670image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-02-08T08:54:28.345792image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-02-08T08:54:29.267953image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-02-08T08:54:30.067192image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-02-08T08:54:31.163335image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-02-08T08:54:32.142077image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-02-08T08:54:33.149449image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-02-08T08:54:26.530988image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-02-08T08:54:27.568338image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-02-08T08:54:28.462021image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-02-08T08:54:29.370777image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-02-08T08:54:30.170851image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-02-08T08:54:31.292168image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-02-08T08:54:32.259154image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Correlations

2025-02-08T08:54:43.497008image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
CARRIER_GROUP_NEWDEST_AIRPORT_IDDISTANCEFREIGHTMAILMONTHORIGIN_AIRPORT_IDPASSENGERSQUARTER
CARRIER_GROUP_NEW1.0000.009-0.1730.1000.1590.0060.007-0.0190.030
DEST_AIRPORT_ID0.0091.0000.0530.0050.001-0.0020.022-0.0290.013
DISTANCE-0.1730.0531.0000.0370.0360.0200.0560.1820.012
FREIGHT0.1000.0050.0371.0000.406-0.027-0.021-0.0690.001
MAIL0.1590.0010.0360.4061.000-0.023-0.0020.1910.002
MONTH0.006-0.0020.020-0.027-0.0231.000-0.0010.0351.000
ORIGIN_AIRPORT_ID0.0070.0220.056-0.021-0.002-0.0011.000-0.0230.013
PASSENGERS-0.019-0.0290.182-0.0690.1910.035-0.0231.0000.036
QUARTER0.0300.0130.0120.0010.0021.0000.0130.0361.000

Missing values

2025-02-08T08:54:33.350341image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
A simple visualization of nullity by column.
2025-02-08T08:54:33.839417image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.

Sample

PASSENGERSFREIGHTMAILDISTANCEUNIQUE_CARRIERUNIQUE_CARRIER_NAMECARRIERCARRIER_NAMECARRIER_GROUP_NEWORIGIN_AIRPORT_IDORIGINORIGIN_CITY_NAMEORIGIN_STATE_ABRORIGIN_STATE_NMDEST_AIRPORT_IDDESTDEST_CITY_NAMEDEST_STATE_ABRDEST_STATE_NMYEARQUARTERMONTH
00.0303913.00.02547.05XUnited Parcel Service5XUnited Parcel Service313891ONTOntario, CACACalifornia14307PVDProvidence, RIRIRhode Island2021411
10.047738.00.02090.05XUnited Parcel Service5XUnited Parcel Service313891ONTOntario, CACACalifornia14122PITPittsburgh, PAPAPennsylvania2021411
20.099406.00.01876.05XUnited Parcel Service5XUnited Parcel Service313891ONTOntario, CACACalifornia12884LANLansing, MIMIMichigan2021411
30.01223074.00.01188.05XUnited Parcel Service5XUnited Parcel Service313891ONTOntario, CACACalifornia11298DFWDallas/Fort Worth, TXTXTexas2021411
40.0475548.0136603.02482.05XUnited Parcel Service5XUnited Parcel Service313891ONTOntario, CACACalifornia10529BDLHartford, CTCTConnecticut2021411
50.0152078.027237.01900.05XUnited Parcel Service5XUnited Parcel Service313891ONTOntario, CACACalifornia10397ATLAtlanta, GAGAGeorgia2021411
60.032432.00.047.05XUnited Parcel Service5XUnited Parcel Service313891ONTOntario, CACACalifornia12892LAXLos Angeles, CACACalifornia2021411
70.092000.00.02297.05XUnited Parcel Service5XUnited Parcel Service313891ONTOntario, CACACalifornia11697FLLFort Lauderdale, FLFLFlorida2021411
80.02056363.04331.0325.05XUnited Parcel Service5XUnited Parcel Service313891ONTOntario, CACACalifornia14107PHXPhoenix, AZAZArizona2021411
90.02312994.033496.02360.05XUnited Parcel Service5XUnited Parcel Service313891ONTOntario, CACACalifornia10299ANCAnchorage, AKAKAlaska2021411
PASSENGERSFREIGHTMAILDISTANCEUNIQUE_CARRIERUNIQUE_CARRIER_NAMECARRIERCARRIER_NAMECARRIER_GROUP_NEWORIGIN_AIRPORT_IDORIGINORIGIN_CITY_NAMEORIGIN_STATE_ABRORIGIN_STATE_NMDEST_AIRPORT_IDDESTDEST_CITY_NAMEDEST_STATE_ABRDEST_STATE_NMYEARQUARTERMONTH
24493771872.0108447.0131.0404.0DLDelta Air Lines Inc.DLDelta Air Lines Inc.313204MCOOrlando, FLFLFlorida10397ATLAtlanta, GAGAGeorgia202137
24493871996.01026752.036439.01235.0AAAmerican Airlines Inc.AAAmerican Airlines Inc.312892LAXLos Angeles, CACACalifornia11298DFWDallas/Fort Worth, TXTXTexas202126
24493972477.087405.015882.0404.0DLDelta Air Lines Inc.DLDelta Air Lines Inc.310397ATLAtlanta, GAGAGeorgia13204MCOOrlando, FLFLFlorida2021411
24494072508.068250.0118.0404.0DLDelta Air Lines Inc.DLDelta Air Lines Inc.313204MCOOrlando, FLFLFlorida10397ATLAtlanta, GAGAGeorgia2021411
24494174310.01263983.0208731.01235.0AAAmerican Airlines Inc.AAAmerican Airlines Inc.312892LAXLos Angeles, CACACalifornia11298DFWDallas/Fort Worth, TXTXTexas2021412
24494277227.01219601.058268.01235.0AAAmerican Airlines Inc.AAAmerican Airlines Inc.312892LAXLos Angeles, CACACalifornia11298DFWDallas/Fort Worth, TXTXTexas202137
24494377370.01195164.057767.01235.0AAAmerican Airlines Inc.AAAmerican Airlines Inc.311298DFWDallas/Fort Worth, TXTXTexas12892LAXLos Angeles, CACACalifornia202137
24494477550.083738.02814.0404.0DLDelta Air Lines Inc.DLDelta Air Lines Inc.313204MCOOrlando, FLFLFlorida10397ATLAtlanta, GAGAGeorgia2021412
24494577656.01273706.0181938.01235.0AAAmerican Airlines Inc.AAAmerican Airlines Inc.311298DFWDallas/Fort Worth, TXTXTexas12892LAXLos Angeles, CACACalifornia2021412
24494680604.068493.012730.0404.0DLDelta Air Lines Inc.DLDelta Air Lines Inc.310397ATLAtlanta, GAGAGeorgia13204MCOOrlando, FLFLFlorida2021412

Duplicate rows

Most frequently occurring

PASSENGERSFREIGHTMAILDISTANCEUNIQUE_CARRIERUNIQUE_CARRIER_NAMECARRIERCARRIER_NAMECARRIER_GROUP_NEWORIGIN_AIRPORT_IDORIGINORIGIN_CITY_NAMEORIGIN_STATE_ABRORIGIN_STATE_NMDEST_AIRPORT_IDDESTDEST_CITY_NAMEDEST_STATE_ABRDEST_STATE_NMYEARQUARTERMONTH# duplicates
110.00.00.063.0UAUnited Air Lines Inc.UAUnited Air Lines Inc.314512RFDRockford, ILILIllinois13930ORDChicago, ILILIllinois2021383
00.00.00.00.02OIsland Air Service2OIsland Air Service510170ADQKodiak, AKAKAlaska10170ADQKodiak, AKAKAlaska2021262
10.00.00.00.05YAtlas Air Inc.5YAtlas Air Inc.310821BWIBaltimore, MDMDMaryland10821BWIBaltimore, MDMDMaryland2021122
20.00.00.00.05YAtlas Air Inc.5YAtlas Air Inc.313788VQQJacksonville, FLFLFlorida13788VQQJacksonville, FLFLFlorida2021262
30.00.00.011.0UAUnited Air Lines Inc.UAUnited Air Lines Inc.313796OAKOakland, CACACalifornia14771SFOSan Francisco, CACACalifornia2021112
40.00.00.023.0V8Iliamna Air TaxiV8Iliamna Air Taxi512748KNKKokhanok, AKAKAlaska12321ILIIliamna, AKAKAlaska20214112
50.00.00.028.0V8Iliamna Air TaxiV8Iliamna Air Taxi514046PDBPedro Bay, AKAKAlaska12321ILIIliamna, AKAKAlaska2021372
60.00.00.037.0V8Iliamna Air TaxiV8Iliamna Air Taxi512321ILIIliamna, AKAKAlaska14268PTAPort Alsworth, AKAKAlaska2021112
70.00.00.037.0V8Iliamna Air TaxiV8Iliamna Air Taxi514268PTAPort Alsworth, AKAKAlaska12321ILIIliamna, AKAKAlaska2021392
80.00.00.037.0V8Iliamna Air TaxiV8Iliamna Air Taxi514268PTAPort Alsworth, AKAKAlaska12321ILIIliamna, AKAKAlaska20214102